用聚类分析百万民宿数据，发现疫情下房源行为模式。
Property Classification of Vacation Rental Properties during Covid-19
- 基于K均值与K中心点聚类，划分房源群体
- 识别出超百万条数据中不同群体的共性特征
- 适合政策制定者与平台运营者参考
本研究倡导利用聚类技术对疫情期间活跃的度假租赁房源进行分类，以揭示其内在模式与行为特征。数据来自英国经济和社会研究委员会资助的消费者数据研究中心（CDRC）与AirDNA的合作项目，涵盖超过一百万条房源及房东数据。通过K均值和K中心点聚类方法，我们识别出具有同质性的群体及其共同特征。研究结果有助于深化对度假租赁评估复杂性的理解，并可能用于制定针对特定聚类群体的精准政策。
原文摘要 · Abstract (English)
This study advocates for employing clustering techniques to classify vacation rental properties active during the Covid pandemic to identify inherent patterns and behaviours. The dataset, a collaboration between the ESRC funded Consumer Data Research Centre (CDRC) and AirDNA, encompasses data for over a million properties and hosts. Utilising K-means and K-medoids clustering techniques, we identify homogenous groups and their common characteristics. Our findings enhance comprehension of the intricacies of vacation rental evaluations and could potentially be utilised in the creation of targeted, cluster-specific policies.
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